MatConNet源代码解读（2）

example/cnn_mnist.m

function [net, info] = cnn_mnist(varargin)%很多人看到varargin就吓住了，其实可以没有参数的%CNN_MNIST  Demonstrates MatConvNet on MNIST%执行vl_setupnn，这么麻烦？run(fullfile(fileparts(mfilename('fullpath')),...  '..', '..', 'matlab', 'vl_setupnn.m')) ;opts.batchNormalization = false ;opts.networkType = 'simplenn' ;[opts, varargin] = vl_argparse(opts, varargin) ;%生成实验中途记录文件的名称，每epoch记录一次。记录数据放在data目录下面sfx = opts.networkType ;if opts.batchNormalization, sfx = [sfx '-bnorm'] ; endopts.expDir = fullfile(vl_rootnn, 'data', ['mnist-baseline-' sfx]) ;[opts, varargin] = vl_argparse(opts, varargin) ;%图像数据库位置opts.dataDir = fullfile(vl_rootnn, 'data', 'mnist') ;opts.imdbPath = fullfile(opts.expDir, 'imdb.mat');opts.train = struct() ;opts = vl_argparse(opts, varargin) ;if ~isfield(opts.train, 'gpus'), opts.train.gpus = []; end;% --------------------------------------------------------------------%                                                         Prepare data% --------------------------------------------------------------------%网络初始化net = cnn_mnist_init('batchNormalization', opts.batchNormalization, ...                     'networkType', opts.networkType) ;%如果有mnist数据库就直接加载，没有就从网上下。有没有觉得matlab一下变得好高档if exist(opts.imdbPath, 'file')  imdb = load(opts.imdbPath) ;else  imdb = getMnistImdb(opts) ;  mkdir(opts.expDir) ;  save(opts.imdbPath, '-struct', 'imdb') ;end%arrayfun以数组的元素作为函数@x的输入，UniformOutput指输出结果的类型是否都相同，为什么要是false呢？没看明白net.meta.classes.name = arrayfun(@(x)sprintf('%d',x),1:10,'UniformOutput',false) ;% --------------------------------------------------------------------%                                                                Train% --------------------------------------------------------------------%开始干正事了switch opts.networkType  case 'simplenn', trainfn = @cnn_train ;  case 'dagnn', trainfn = @cnn_train_dag ;end%trainfn就是cnn_train啦，val参数有什么用[net, info] = trainfn(net, imdb, getBatch(opts), ...  'expDir', opts.expDir, ...  net.meta.trainOpts, ...  opts.train, ...  'val', find(imdb.images.set == 3)) ;% 取batch数据，不会吧，分割batch还要自己来。仔细看输出是个函数指针，说明实际batch是自动抽取的%--------------------------------------------------------------------function fn = getBatch(opts)% --------------------------------------------------------------------switch lower(opts.networkType)  case 'simplenn'    fn = @(x,y) getSimpleNNBatch(x,y) ;  case 'dagnn'    bopts = struct('numGpus', numel(opts.train.gpus)) ;    fn = @(x,y) getDagNNBatch(bopts,x,y) ;end% --------------------------------------------------------------------function [images, labels] = getSimpleNNBatch(imdb, batch)% --------------------------------------------------------------------images = imdb.images.data(:,:,:,batch) ;labels = imdb.images.labels(1,batch) ;% --------------------------------------------------------------------function inputs = getDagNNBatch(opts, imdb, batch)% --------------------------------------------------------------------images = imdb.images.data(:,:,:,batch) ;labels = imdb.images.labels(1,batch) ;if opts.numGpus > 0  images = gpuArray(images) ;endinputs = {'input', images, 'label', labels} ;%下载MnistImdb，话说Lecun也就是靠这个数据库一战成名，1998年那篇文章到底做了多少乱七八糟的实验啊~% --------------------------------------------------------------------function imdb = getMnistImdb(opts)% --------------------------------------------------------------------% Preapre the imdb structure, returns image data with mean image subtractedfiles = {'train-images-idx3-ubyte', ...         'train-labels-idx1-ubyte', ...         't10k-images-idx3-ubyte', ...         't10k-labels-idx1-ubyte'} ;if ~exist(opts.dataDir, 'dir')  mkdir(opts.dataDir) ;endfor i=1:4  if ~exist(fullfile(opts.dataDir, files{i}), 'file')    url = sprintf('http://yann.lecun.com/exdb/mnist/%s.gz',files{i}) ;    fprintf('downloading %s\n', url) ;    gunzip(url, opts.dataDir) ;  endendf=fopen(fullfile(opts.dataDir, 'train-images-idx3-ubyte'),'r') ;x1=fread(f,inf,'uint8');fclose(f) ;x1=permute(reshape(x1(17:end),28,28,60e3),[2 1 3]) ;f=fopen(fullfile(opts.dataDir, 't10k-images-idx3-ubyte'),'r') ;x2=fread(f,inf,'uint8');fclose(f) ;x2=permute(reshape(x2(17:end),28,28,10e3),[2 1 3]) ;f=fopen(fullfile(opts.dataDir, 'train-labels-idx1-ubyte'),'r') ;y1=fread(f,inf,'uint8');fclose(f) ;y1=double(y1(9:end)')+1 ;f=fopen(fullfile(opts.dataDir, 't10k-labels-idx1-ubyte'),'r') ;y2=fread(f,inf,'uint8');fclose(f) ;y2=double(y2(9:end)')+1 ;%训练集为1，测试集为3set = [ones(1,numel(y1)) 3*ones(1,numel(y2))];data = single(reshape(cat(3, x1, x2),28,28,1,[]));dataMean = mean(data(:,:,:,set == 1), 4);%这个函数牛逼了，可以理解为将dataMean扩展至与data同维数，然后逐点执行minus操作。实际可能分布式计算，好牛叉！data = bsxfun(@minus, data, dataMean) ;imdb.images.data = data ;imdb.images.data_mean = dataMean;imdb.images.labels = cat(2, y1, y2) ;imdb.images.set = set ;%最难的在这里，‘val’是什么意思一直没搞懂！imdb.meta.sets = {'train', 'val', 'test'} ;imdb.meta.classes = arrayfun(@(x)sprintf('%d',x),0:9,'uniformoutput',false) ;